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onimai
Browse files- grasswonder-umamusume/README.md +2 -2
- onimai/README.md +67 -0
- onimai/samples/00026-4010692159.png +0 -0
- onimai/samples/00030-286171376.png +0 -0
- onimai/samples/00034-2431887953.png +0 -0
- onimai/samples/grid-00010-492069042.png +0 -0
- onimai/samples/grid-00017-492069042.png +0 -0
- suremio-nozomizo-eilanya-maplesally/.README.md.swp +0 -0
- suremio-nozomizo-eilanya-maplesally/README.md +5 -2
grasswonder-umamusume/README.md
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@@ -35,7 +35,7 @@ Trained with [Kohya trainer](https://github.com/Linaqruf/kohya-trainer)
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### LoRA
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train)
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- learning rate 1e-4
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- batch size 6
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- clip skip 2
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- number of training steps 7520 (20 epochs)
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*Examples*
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### LoRA
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train)
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- learning rate 1e-4
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- batch size 6
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- clip skip 2
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- number of training steps 7520/6 (20 epochs)
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*Examples*
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onimai/README.md
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This folder contains models trained for the two characters oyama mahiro and oyama mihari.
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Trigger words are
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- oyama mahiro
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- oyama mihari
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To get anime style you can add `aniscreen`
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At this point I feel like having oyama in the trigger is probably a bad idea because it seems to cause more character blending.
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### Dataset
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Total size 338
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screenshots 127
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- Mahiro: 51
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- Mihari: 46
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- Mahiro + Mihari: 30
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fanart 92
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- Mahiro: 68
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- Mihari: 8
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- Mahiro + Mihari: 16
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Regularization 119
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For training the following repeat is used
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- 1 for Mahiro and reg
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- 2 for Mihari
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- 4 for Mahiro + Mihari
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### Base model
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[NMFSAN](https://huggingface.co/Crosstyan/BPModel/blob/main/NMFSAN/README.md)
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### LoRA
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train)
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- training of text encoder turned on
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- network dimension 64
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- learning rate scheduler constant
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- learning rate 1e-4 and 1e-5 (two separate runs)
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- batch size 7
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- clip skip 2
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- number of training epochs 45
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### Comparaison
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learning rate 1e-4
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learning rate 1e-5
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Normally with 2 repeats and 45 epochs we should have perfectly learned the character with dreambooth (using typically lr=1e-6), but here with lr=1e-5 it does not seem to work very well. lr=1e-4 produces quite correct results but there is a risk of overfitting.
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### Examples
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onimai/samples/00026-4010692159.png
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onimai/samples/00030-286171376.png
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onimai/samples/00034-2431887953.png
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onimai/samples/grid-00010-492069042.png
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onimai/samples/grid-00017-492069042.png
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suremio-nozomizo-eilanya-maplesally/.README.md.swp
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Binary file (12.3 kB)
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suremio-nozomizo-eilanya-maplesally/README.md
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@@ -44,6 +44,7 @@ Regularization 276
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[NMFSAN](https://huggingface.co/Crosstyan/BPModel/blob/main/NMFSAN/README.md) so you can have different styles
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### Native training
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Trained with [Kohya trainer](https://github.com/Linaqruf/kohya-trainer)
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-
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train)
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- learning rate 1e-4
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- batch size 6
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- clip skip 2
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- number of training steps 69700 (50 epochs)
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*Examples*
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[NMFSAN](https://huggingface.co/Crosstyan/BPModel/blob/main/NMFSAN/README.md) so you can have different styles
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### Native training
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Trained with [Kohya trainer](https://github.com/Linaqruf/kohya-trainer)
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### LoRA
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Please refer to [LoRA Training Guide](https://rentry.org/lora_train)
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- learning rate 1e-4
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- batch size 6
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- clip skip 2
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- number of training steps 69700/6 (50 epochs)
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*Examples*
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